To obtain information about the full state of the system, it is
necessary to perform sampling over numerous spin configurations. While
this model is preferably suited to the determination of ground state
properties, after some modifications it has also been used to
determine dynamical properties of QMB
systems.[1]}, [2]}, [3]} Recently,
convolutional neural network have also been used to map input QMB spin
configurations to probability amplitudes.[4]} In spite
of the success of these approaches, these models still face similar
challenges as other computational methods, namely that accurately
representing the system state becomes computationally demanding as the
system size grows.
